This study was carried out to evaluate F1 single cross-maize hybrids in four crop growing seasons (2010–2012). Morphological traits and physiological parameters of twelve maize hybrids were evaluated (i) to construct seed yield equation and (ii) to determine grain yield attributing traits of well-performing maize genotype using a previously unexplored method of two-way hierarchical clustering. In seed yield predicting equation photosynthetic rate contributed the highest variation (46%). Principal component analysis data showed that investigated traits contributed up to 90.55% variation in dependent structure. From factor analysis, we found that factor 1 contributed 49.6% variation (
Maize (
Considering these important, although partially resolved, research aspects, we conducted a field study in four consecutive crop growing seasons (2010-2011 and 2011-2012). The objective of the present study was to demonstrate combined effect of seasonal variation on maize grain yield and its attributing traits in maize hybrids under high temperature (>45°C) and drought stress conditions. The two-way hierarchical clustering was used for making clusters within and among the genotypes and traits for effective selection. This allowed us to develop a model for predicting grain yield per plant thus selecting the most promising traits of high yielding maize hybrids under investigated stress environments.
The four maize inbred lines: Agaiti-85 (P1), Golden (P2), Soneri (P3) and Sultan (P4) from F7 population (99.25% purity) under high temperature (>45°C) and drought stress conditions were selected during 2009-2010 in the University of Agriculture, Faisalabad
Parental lines were crossed in a complete Diallel fashion to develop F1 single cross hybrids. The maize hybrids were H1 (Agaiti-85 × Golden), H2 (Agaiti-85 × Soneri), H3 (Agaiti-85 × Sultan), H4 (Golden × Agaiti-85), H5 (Golden × Soneri), H6 (Golden × Sultan), H7 (Soneri × Agaiti-85), H8 (Soneri × Golden), H9 (Soneri × Sultan), H10 (Sultan × Agaiti-85), H11 (Sultan × Golden), and H12 (Sultan × Soneri).
Maize plants were cultivated in the field in a randomized complete block design (RCBD) with five replicates per genotype. Hybrids were evaluated for grain yield in four consecutive crop growing seasons: 2011 and 2012 (February and August). Combined data was used for statistical analysis to reduce effect of crop growing seasons to optimize grain yield and its attributing traits in either of crop growing seasons.
Each plot was of 3 m × 3 m size. Row-to-row and plant-to-plant distances were 75 cm and 15 cm, respectively, with each row having 15 plants. Seed sowing was done using a dibbler. Two seeds/hill of each genotype were sown and after 20 days thinned up to one plant/hill. All recommended cultural and agronomic practices such as howing, mowing, irrigation, fertilizer application, and weeding were done during crop growing seasons.
Random sampling of 10 plants/plot of each genotype was done to measure the following traits: chlorophyll content (Ch.c.), plant height (PH), stem diameter (SD), fresh stem weight (FSW), number of leaves per plant (nlp), fresh leaves weight (FLW), fresh leaves weight to stem weight ratio (FLSWR), leaf area (LA), leaf length (LL) from acute angle, leaf width (LW), transpiration rate (
The data of different crop growth stages such as seedling emergence, silking and physiological maturity of maize plants were recorded for each experimental plot.
Plant height, leaf length, and leaf width were measured using a measuring tape. The fresh leaf weight and fresh stem weight were recorded. Digital vernier caliper was used to measure stem diameter by computing average values of measured stem diameters at basal, middle, and top portions.
Green leaf area per plant was determined according to
At harvesting, 10 plants of each genotype were sampled. Cobs from plants of each genotype were harvested to record the grain yield per plant. The moisture level of the grains was adjusted (14–15.5%) to 140 g/kg [
A two-way analysis of variance (ANOVA) was used to determine significant differences for grain yield and its attributing traits. The stepwise multiple linear regression was performed between the seed yield and its attributing traits to construct seed yield equation. GenStat version 12 software was used for statistical analysis of data. Multivariate analysis was performed (PROC Mixed SAS version 9.1, SAS Institute [
In the present study, analysis of variance of all studied traits in maize hybrids (Table S1, in Supplementary Material available online at
For hybrid H12
Stepwise multiple linear regression of grain yield attributing traits (see Section
Variable | Regression coefficients |
|
Cumulative |
Partial |
||
---|---|---|---|---|---|---|
|
SE (±) | |||||
|
|
|
0.01436 |
|
|
46.9% |
|
|
|
0.03239 |
|
|
33.4% |
|
Ch.c. |
|
0.00200 |
|
|
30.4% |
|
FSW |
|
0.01756 |
|
|
27.1% |
|
nlp |
|
0.02666 |
|
|
25.8% |
|
SD |
|
|
|
|
25.6% |
|
|
|
0.00797 |
|
|
25.4% |
|
LW |
|
0.00385 |
|
|
24.1% |
|
PH |
|
0.01899 |
|
|
15.1% |
|
FLSWR |
|
0.00358 |
|
|
13.1% |
|
LA |
|
0.00494 |
|
|
11.7% |
|
LL |
|
0.20570 |
|
|
8.7% |
|
|
|
0.07671 |
|
|
7.3% |
|
FLW |
|
|
|
|
5.8% |
|
LT |
|
0.01183 |
|
|
0.8% |
Intercept = −22.23. Multiple
The best prediction equation for grain yield in the present study was as follows:
We showed that photosynthetic rate contributed the maximum variation (Table
A PCA was performed using various traits under investigation (Supplementary Material, Table S4) and three principal components (PCs) were observed: PC1, PC2, and PC3 (Figure
Factor loadings of grain yield attributing morphophysiological and agronomic traits (see Section
Variables | Loadings | % of total communality |
---|---|---|
Factor 1 | 49.85 | |
nlp | 0.818 | |
PH | 0.824 | |
SD | 0.502 | |
FLW | 0.790 | |
LA | 0.764 | |
|
0.866 | |
|
0.759 | |
|
0.803 | |
|
||
Factor 2 | 29.47 | |
Ch.c. |
|
|
|
|
|
FLSWR |
|
|
|
||
Factor 3 | 11.22 | |
LL | 0.373 | |
LT | 0.171 | |
Cumulative variance | 90.55 |
(a) Principal component analysis of grain yield and its attributing traits. (b) Scree plot and respective eigenvalues (see Section
Our findings are well supported by Filipović et al. [
Dendrogram analysis based on two-way hierarchal clustering. Association of hybrids and traits based on genetic basis (see Section
The present study provided insights into drought tolerant and heat resistant maize hybrids for arid/semiarid regions like Pakistan. A new way of two-way hierarchical clustering was used which enabled us to develop a relationship among the hybrids and morphophysiological traits. We used a combination of physiological strategies and breeding methods to evaluate the maize hybrids in four consecutive crop growing seasons over a period of two years (2010-2011 and 2011-2012). The results showed that H12 possessed the highest grain yield under high temperature stress and low irrigation regime. Our findings also provide insights to understand GT factors, which are considered to be valuable for future breeding programs in maize. However, further research is warranted on different locations and climatic conditions.
Duncan’s Multiple Range Test
Principal component analysis
Factor analysis.
The authors declare that there is no conflict of interests regarding the publication of this paper.
The authors are grateful to Department of Plant Breeding and Genetics, University of Agriculture Faisalabad, Pakistan, for providing necessary genome of maize and research facilities. The first author is grateful to Associate Professor Dr. Anwar-ul-Haq (Institute of Soil and Environmental Sciences, University of Agriculture Faisalabad, Pakistan) for providing IRGA and SPAD chlorophyll meter.